Victor Nozais

Institut des Maladies Neurodegeneratives | IMN · Neurofunctional imaging group (GIN)

Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifes...

Over the past two decades, the study of resting-state functional magnetic resonance imaging has revealed that functional connectivity within and between networks is linked to cognitive states and pathologies. However, the white matter connections supporting this connectivity remain only partially described. We developed a method to jointly map the...

Integrating the underlying brain circuit's structural and functional architecture is required to explore the functional organization of cognitive networks properly. In that regard, we recently introduced the Functionnectome. This structural-functional method combines an fMRI acquisition with tractography-derived white matter connectivity data to ma...

These authors contributed equally to this work. Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neu-roimaging data. Previous works have demonstrated a strong relat...

Thirty years of functional neuroimaging have been accumulating a wealth of evidence of the intricate relationship between structure and function. However, potential overlap, independence, granularity of and gaps between functions remain poorly understood at the global level. In this study, we extracted the latent structure of the current brain-cogn...

The contribution of structural connectivity to functional connectivity dynamics is still far from being fully elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two in...

Over the past two decades, the study of resting-state functional magnetic resonance imaging (fMRI) has revealed the existence of multiple brain areas displaying synchronous functional blood oxygen level-dependent signals (BOLD)-resting-state networks (RSNs). The variation in functional connectivity between the different areas of a resting-state net...

Stroke significantly impacts quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need for a better prediction of long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter...

The contribution of structural connectivity to functional connectivity dynamics is still far from being fully elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two in...

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to an...

In recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomi...

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18–35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described...

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832...

In recent years, the field of functional neuroimaging has moved from a pure localisationist approach of isolated functional brain regions to a more integrated view of those regions within functional networks. The methods used to investigate such networks, however, rely on local signals in grey matter and are limited in identifying anatomical circui...

In recent years, the field of functional neuroimaging has moved from a pure localisationist approach of isolated functional brain regions to a more integrated view of those regions within functional networks. The methods used to investigate such networks, however, rely on local signals in grey matter and are limited in identifying anatomical circui...

In recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomi...

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832...

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to an...

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1,870 young healthy adults, aged 18 to 35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility weighted (SWI), and resting-state functional imaging modalities. Here, we descri...